code: def encode_existing(self, df, column_name):
      # fill missing values with the a new category missing
      df[column_name] = df[column_name].fillna("missing")
  
      en = (
          self.one_hot_encoder[column_name]
          .fit_transform(df[[column_name]])
          .toarray()
      )
      df_en = pd.DataFrame(en)
  
      # change column_names
      df_en = df_en.rename(columns=self.column_names[column_name])
  
      # add new column and merge enc with the existing dataframe
      final_df = df.join(df_en)
  
      final_df = final_df.drop(column_name, axis=1)
  
      return final_df
  
file_path: explainx/lib/encode_decode_cat_col.py
imports: from imports import *
  from sklearn.preprocessing import OneHotEncoder
  import numpy
label_desc: Use image node_id 4 for calling the encode_decode_cat_col obj's underlying member method code with example usage: obj.encode_existing(df, column_name) and returns: final_df